Imitating Tool-Based Garment Folding From a Single Visual Observation Using Hand-Object Graph Dynamics

Peng Zhou, Jiaming Qi, Anqing Duan, Shengzeng Huo, Zeyu Wu, David Navarro-Alarcon

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Garment folding is a ubiquitous domestic task that is difficult to automate due to the highly deformable nature of fabrics. In this article, we propose a novel method of learning from demonstrations that enables robots to autonomously manipulate an assistive tool to fold garments. In contrast to traditional methods (that rely on low-level pixel features), our proposed solution uses a dense visual descriptor to encode the demonstration into a high-level <italic>hand-object graph</italic> (HoG) that allows to efficiently represent the interactions between the manipulated tool and robots. With that, we leverage graph neural network to autonomously learn the forward dynamics model from HoGs, then, given only a single demonstration, the imitation policy is optimized with a model predictive controller to accomplish the folding task. To validate the proposed approach, we conducted a detailed experimental study on a robotic platform instrumented with vision sensors and a custom-made end-effector that interacts with the folding board.

Original languageEnglish
Pages (from-to)1-12
Number of pages12
JournalIEEE Transactions on Industrial Informatics
DOIs
Publication statusAccepted/In press - 2023

Keywords

  • Cloth folding
  • Clothing
  • graph dynamics model
  • hand-object graph (HoG)
  • imitation learning (IL)
  • Manipulator dynamics
  • Predictive models
  • Robots
  • Task analysis
  • tool manipulation
  • Trajectory
  • Visualization

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Information Systems
  • Computer Science Applications
  • Electrical and Electronic Engineering

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